Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations5010
Missing cells991
Missing cells (%)0.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory5.2 MiB
Average record size in memory1.1 KiB

Variable types

DateTime1
Categorical10
Text4
Numeric7

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
NetAmount is highly overall correlated with QuantitySold and 1 other fieldsHigh correlation
ProductCategory is highly overall correlated with ProductName and 2 other fieldsHigh correlation
ProductName is highly overall correlated with ProductCategory and 2 other fieldsHigh correlation
ProductPrice is highly overall correlated with ProductCategory and 2 other fieldsHigh correlation
ProductSubCategory is highly overall correlated with ProductCategory and 2 other fieldsHigh correlation
QuantitySold is highly overall correlated with NetAmount and 1 other fieldsHigh correlation
StockOnHand is highly overall correlated with StockReceivedHigh correlation
StockReceived is highly overall correlated with StockOnHand and 1 other fieldsHigh correlation
StockSold is highly overall correlated with StockReceivedHigh correlation
SupplierContact is highly overall correlated with SupplierLocation and 1 other fieldsHigh correlation
SupplierLocation is highly overall correlated with SupplierContact and 1 other fieldsHigh correlation
SupplierName is highly overall correlated with SupplierContact and 1 other fieldsHigh correlation
TotalAmount is highly overall correlated with NetAmount and 1 other fieldsHigh correlation
CustomerEmail has 507 (10.1%) missing values Missing
SupplierContact has 484 (9.7%) missing values Missing

Reproduction

Analysis started2024-12-20 14:45:51.191513
Analysis finished2024-12-20 14:45:56.866254
Duration5.67 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Date
Date

Distinct729
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
Minimum2021-09-28 00:00:00
Maximum2023-09-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-20T15:45:56.934867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:57.045169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ProductName
Categorical

High correlation 

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size314.2 KiB
NonExistentProduct
462 
Anthony
 
251
Robert
 
246
Wanda
 
246
Angela
 
244
Other values (16)
3561 

Length

Max length18
Median length8
Mean length7.2007984
Min length3

Characters and Unicode

Total characters36076
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNathaniel
2nd rowNonExistentProduct
3rd rowAngela
4th rowAmy
5th rowNathaniel

Common Values

ValueCountFrequency (%)
NonExistentProduct 462
 
9.2%
Anthony 251
 
5.0%
Robert 246
 
4.9%
Wanda 246
 
4.9%
Angela 244
 
4.9%
Fred 240
 
4.8%
Shannon 239
 
4.8%
Michael 236
 
4.7%
Shawn 235
 
4.7%
Marvin 233
 
4.7%
Other values (11) 2378
47.5%

Length

2024-12-20T15:45:57.155345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nonexistentproduct 462
 
9.2%
anthony 251
 
5.0%
robert 246
 
4.9%
wanda 246
 
4.9%
angela 244
 
4.9%
fred 240
 
4.8%
shannon 239
 
4.8%
michael 236
 
4.7%
shawn 235
 
4.7%
marvin 233
 
4.7%
Other values (11) 2378
47.5%

Most occurring characters

ValueCountFrequency (%)
a 4011
 
11.1%
n 4000
 
11.1%
t 3417
 
9.5%
e 2706
 
7.5%
i 2687
 
7.4%
h 1843
 
5.1%
o 1660
 
4.6%
r 1618
 
4.5%
l 1122
 
3.1%
m 1057
 
2.9%
Other values (24) 11955
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4011
 
11.1%
n 4000
 
11.1%
t 3417
 
9.5%
e 2706
 
7.5%
i 2687
 
7.4%
h 1843
 
5.1%
o 1660
 
4.6%
r 1618
 
4.5%
l 1122
 
3.1%
m 1057
 
2.9%
Other values (24) 11955
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4011
 
11.1%
n 4000
 
11.1%
t 3417
 
9.5%
e 2706
 
7.5%
i 2687
 
7.4%
h 1843
 
5.1%
o 1660
 
4.6%
r 1618
 
4.5%
l 1122
 
3.1%
m 1057
 
2.9%
Other values (24) 11955
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4011
 
11.1%
n 4000
 
11.1%
t 3417
 
9.5%
e 2706
 
7.5%
i 2687
 
7.4%
h 1843
 
5.1%
o 1660
 
4.6%
r 1618
 
4.5%
l 1122
 
3.1%
m 1057
 
2.9%
Other values (24) 11955
33.1%

ProductCategory
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size323.4 KiB
Electronics
1575 
Books
1098 
Toys
725 
Home & Garden
648 
InvalidCategory
516 

Length

Max length15
Median length13
Mean length9.0744511
Min length4

Characters and Unicode

Total characters45463
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronics
2nd rowElectronics
3rd rowInvalidCategory
4th rowHome & Garden
5th rowElectronics

Common Values

ValueCountFrequency (%)
Electronics 1575
31.4%
Books 1098
21.9%
Toys 725
14.5%
Home & Garden 648
12.9%
InvalidCategory 516
 
10.3%
Clothing 448
 
8.9%

Length

2024-12-20T15:45:57.252615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T15:45:57.475603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
electronics 1575
25.0%
books 1098
17.4%
toys 725
11.5%
home 648
10.3%
648
10.3%
garden 648
10.3%
invalidcategory 516
 
8.2%
clothing 448
 
7.1%

Most occurring characters

ValueCountFrequency (%)
o 6108
13.4%
s 3398
 
7.5%
e 3387
 
7.5%
n 3187
 
7.0%
c 3150
 
6.9%
r 2739
 
6.0%
t 2539
 
5.6%
l 2539
 
5.6%
i 2539
 
5.6%
a 1680
 
3.7%
Other values (16) 14197
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6108
13.4%
s 3398
 
7.5%
e 3387
 
7.5%
n 3187
 
7.0%
c 3150
 
6.9%
r 2739
 
6.0%
t 2539
 
5.6%
l 2539
 
5.6%
i 2539
 
5.6%
a 1680
 
3.7%
Other values (16) 14197
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6108
13.4%
s 3398
 
7.5%
e 3387
 
7.5%
n 3187
 
7.0%
c 3150
 
6.9%
r 2739
 
6.0%
t 2539
 
5.6%
l 2539
 
5.6%
i 2539
 
5.6%
a 1680
 
3.7%
Other values (16) 14197
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6108
13.4%
s 3398
 
7.5%
e 3387
 
7.5%
n 3187
 
7.0%
c 3150
 
6.9%
r 2739
 
6.0%
t 2539
 
5.6%
l 2539
 
5.6%
i 2539
 
5.6%
a 1680
 
3.7%
Other values (16) 14197
31.2%

ProductSubCategory
Categorical

High correlation 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size321.1 KiB
Headphones
1227 
Academic
536 
Shirt
501 
Fiction
455 
Educational
274 
Other values (8)
2017 

Length

Max length15
Median length11
Mean length8.5968064
Min length5

Characters and Unicode

Total characters43070
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCamera
2nd rowMobile
3rd rowAction Figures
4th rowDecor
5th rowCamera

Common Values

ValueCountFrequency (%)
Headphones 1227
24.5%
Academic 536
10.7%
Shirt 501
10.0%
Fiction 455
 
9.1%
Educational 274
 
5.5%
Dolls 273
 
5.4%
Mobile 272
 
5.4%
Action Figures 258
 
5.1%
Gardening Tools 255
 
5.1%
Decor 246
 
4.9%
Other values (3) 713
14.2%

Length

2024-12-20T15:45:57.585098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
headphones 1227
22.2%
academic 536
9.7%
shirt 501
9.1%
fiction 455
 
8.2%
educational 274
 
5.0%
dolls 273
 
4.9%
mobile 272
 
4.9%
action 258
 
4.7%
figures 258
 
4.7%
gardening 255
 
4.6%
Other values (5) 1214
22.0%

Most occurring characters

ValueCountFrequency (%)
e 4503
 
10.5%
o 3977
 
9.2%
i 3963
 
9.2%
n 3423
 
7.9%
a 3056
 
7.1%
c 2536
 
5.9%
d 2292
 
5.3%
s 2013
 
4.7%
r 1979
 
4.6%
t 1956
 
4.5%
Other values (21) 13372
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4503
 
10.5%
o 3977
 
9.2%
i 3963
 
9.2%
n 3423
 
7.9%
a 3056
 
7.1%
c 2536
 
5.9%
d 2292
 
5.3%
s 2013
 
4.7%
r 1979
 
4.6%
t 1956
 
4.5%
Other values (21) 13372
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4503
 
10.5%
o 3977
 
9.2%
i 3963
 
9.2%
n 3423
 
7.9%
a 3056
 
7.1%
c 2536
 
5.9%
d 2292
 
5.3%
s 2013
 
4.7%
r 1979
 
4.6%
t 1956
 
4.5%
Other values (21) 13372
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4503
 
10.5%
o 3977
 
9.2%
i 3963
 
9.2%
n 3423
 
7.9%
a 3056
 
7.1%
c 2536
 
5.9%
d 2292
 
5.3%
s 2013
 
4.7%
r 1979
 
4.6%
t 1956
 
4.5%
Other values (21) 13372
31.0%

ProductPrice
Categorical

High correlation 

Distinct43
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.8 KiB
InvalidPrice
449 
0.01
 
274
99999.99
 
252
46.23
 
196
693.39
 
196
Other values (38)
3643 

Length

Max length17
Median length6
Mean length6.498004
Min length4

Characters and Unicode

Total characters32555
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.01
2nd row847.43
3rd row386.57
4th row364.01
5th row652.35

Common Values

ValueCountFrequency (%)
InvalidPrice 449
 
9.0%
0.01 274
 
5.5%
99999.99 252
 
5.0%
46.23 196
 
3.9%
693.39 196
 
3.9%
386.57 196
 
3.9%
461.09 192
 
3.8%
847.43 191
 
3.8%
268.61 186
 
3.7%
951.53 186
 
3.7%
Other values (33) 2692
53.7%

Length

2024-12-20T15:45:57.679580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
invalidprice 449
 
9.0%
0.01 274
 
5.5%
99999.99 252
 
5.0%
46.23 196
 
3.9%
693.39 196
 
3.9%
386.57 196
 
3.9%
461.09 192
 
3.8%
847.43 191
 
3.8%
268.61 186
 
3.7%
951.53 186
 
3.7%
Other values (33) 2692
53.7%

Most occurring characters

ValueCountFrequency (%)
. 4561
14.0%
9 3430
10.5%
1 3023
9.3%
3 2843
8.7%
6 2644
8.1%
0 2552
7.8%
8 2043
 
6.3%
4 1833
 
5.6%
2 1808
 
5.6%
5 1794
 
5.5%
Other values (12) 6024
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4561
14.0%
9 3430
10.5%
1 3023
9.3%
3 2843
8.7%
6 2644
8.1%
0 2552
7.8%
8 2043
 
6.3%
4 1833
 
5.6%
2 1808
 
5.6%
5 1794
 
5.5%
Other values (12) 6024
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4561
14.0%
9 3430
10.5%
1 3023
9.3%
3 2843
8.7%
6 2644
8.1%
0 2552
7.8%
8 2043
 
6.3%
4 1833
 
5.6%
2 1808
 
5.6%
5 1794
 
5.5%
Other values (12) 6024
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4561
14.0%
9 3430
10.5%
1 3023
9.3%
3 2843
8.7%
6 2644
8.1%
0 2552
7.8%
8 2043
 
6.3%
4 1833
 
5.6%
2 1808
 
5.6%
5 1794
 
5.5%
Other values (12) 6024
18.5%
Distinct4846
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size343.9 KiB
2024-12-20T15:45:57.911549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length28
Median length24
Mean length13.268862
Min length7

Characters and Unicode

Total characters66477
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4707 ?
Unique (%)94.0%

Sample

1st rowColleen Kelly
2nd rowJoel Wright
3rd rowThomas Sawyer
4th rowTyler Gardner
5th rowMeagan Peterson
ValueCountFrequency (%)
smith 119
 
1.2%
michael 110
 
1.1%
johnson 84
 
0.8%
james 82
 
0.8%
thomas 80
 
0.8%
john 77
 
0.8%
jennifer 72
 
0.7%
christopher 72
 
0.7%
david 66
 
0.6%
williams 63
 
0.6%
Other values (1420) 9437
92.0%
2024-12-20T15:45:58.238932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6188
 
9.3%
a 6049
 
9.1%
5252
 
7.9%
n 4930
 
7.4%
r 4791
 
7.2%
i 4078
 
6.1%
o 3582
 
5.4%
l 3388
 
5.1%
s 2982
 
4.5%
h 2271
 
3.4%
Other values (42) 22966
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6188
 
9.3%
a 6049
 
9.1%
5252
 
7.9%
n 4930
 
7.4%
r 4791
 
7.2%
i 4078
 
6.1%
o 3582
 
5.4%
l 3388
 
5.1%
s 2982
 
4.5%
h 2271
 
3.4%
Other values (42) 22966
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6188
 
9.3%
a 6049
 
9.1%
5252
 
7.9%
n 4930
 
7.4%
r 4791
 
7.2%
i 4078
 
6.1%
o 3582
 
5.4%
l 3388
 
5.1%
s 2982
 
4.5%
h 2271
 
3.4%
Other values (42) 22966
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6188
 
9.3%
a 6049
 
9.1%
5252
 
7.9%
n 4930
 
7.4%
r 4791
 
7.2%
i 4078
 
6.1%
o 3582
 
5.4%
l 3388
 
5.1%
s 2982
 
4.5%
h 2271
 
3.4%
Other values (42) 22966
34.5%

CustomerEmail
Text

Missing 

Distinct4450
Distinct (%)98.8%
Missing507
Missing (%)10.1%
Memory size362.7 KiB
2024-12-20T15:45:58.406568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length21.849656
Min length15

Characters and Unicode

Total characters98389
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4408 ?
Unique (%)97.9%

Sample

1st rowmaryhurst@example.org
2nd rowsandersvictoria@example.org
3rd rowospence@example.net
4th rowchristopherjohnson@example.com
5th rowepowell@example.net
ValueCountFrequency (%)
garciashawn@example.com 11
 
0.2%
xjackson@example.net 3
 
0.1%
fwilliams@example.net 3
 
0.1%
taylorrobert@example.com 2
 
< 0.1%
ijones@example.com 2
 
< 0.1%
jenniferreed@example.com 2
 
< 0.1%
wrightjennifer@example.net 2
 
< 0.1%
rtaylor@example.org 2
 
< 0.1%
osmith@example.com 2
 
< 0.1%
rallen@example.org 2
 
< 0.1%
Other values (4440) 4472
99.3%
2024-12-20T15:45:58.696983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14830
15.1%
a 9158
 
9.3%
m 7680
 
7.8%
l 7142
 
7.3%
o 5624
 
5.7%
r 5230
 
5.3%
p 5088
 
5.2%
n 4925
 
5.0%
x 4610
 
4.7%
@ 4503
 
4.6%
Other values (28) 29599
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14830
15.1%
a 9158
 
9.3%
m 7680
 
7.8%
l 7142
 
7.3%
o 5624
 
5.7%
r 5230
 
5.3%
p 5088
 
5.2%
n 4925
 
5.0%
x 4610
 
4.7%
@ 4503
 
4.6%
Other values (28) 29599
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14830
15.1%
a 9158
 
9.3%
m 7680
 
7.8%
l 7142
 
7.3%
o 5624
 
5.7%
r 5230
 
5.3%
p 5088
 
5.2%
n 4925
 
5.0%
x 4610
 
4.7%
@ 4503
 
4.6%
Other values (28) 29599
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14830
15.1%
a 9158
 
9.3%
m 7680
 
7.8%
l 7142
 
7.3%
o 5624
 
5.7%
r 5230
 
5.3%
p 5088
 
5.2%
n 4925
 
5.0%
x 4610
 
4.7%
@ 4503
 
4.6%
Other values (28) 29599
30.1%
Distinct5000
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size497.2 KiB
2024-12-20T15:45:58.943522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length65
Median length56
Mean length44.60519
Min length22

Characters and Unicode

Total characters223472
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4999 ?
Unique (%)99.8%

Sample

1st row354 Mcdowell Turnpike, Port Charles, CT 95318
2nd row24740 Fox Villages, New Tracie, MA 53038
3rd row769 Joe Trail, East Terri, CA 43813
4th row27783 Olivia Centers, Williamsmouth, AL 09809
5th row25357 Blackwell Locks, Andreabury, MH 27857
ValueCountFrequency (%)
suite 1103
 
3.0%
apt 1096
 
3.0%
port 372
 
1.0%
box 364
 
1.0%
new 343
 
0.9%
south 343
 
0.9%
lake 326
 
0.9%
east 320
 
0.9%
north 304
 
0.8%
west 299
 
0.8%
Other values (13437) 32011
86.8%
2024-12-20T15:45:59.380525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31871
 
14.3%
e 11462
 
5.1%
, 9667
 
4.3%
a 9275
 
4.2%
t 8835
 
4.0%
r 8212
 
3.7%
i 7308
 
3.3%
o 7256
 
3.2%
n 6972
 
3.1%
s 6018
 
2.7%
Other values (54) 116596
52.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31871
 
14.3%
e 11462
 
5.1%
, 9667
 
4.3%
a 9275
 
4.2%
t 8835
 
4.0%
r 8212
 
3.7%
i 7308
 
3.3%
o 7256
 
3.2%
n 6972
 
3.1%
s 6018
 
2.7%
Other values (54) 116596
52.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31871
 
14.3%
e 11462
 
5.1%
, 9667
 
4.3%
a 9275
 
4.2%
t 8835
 
4.0%
r 8212
 
3.7%
i 7308
 
3.3%
o 7256
 
3.2%
n 6972
 
3.1%
s 6018
 
2.7%
Other values (54) 116596
52.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31871
 
14.3%
e 11462
 
5.1%
, 9667
 
4.3%
a 9275
 
4.2%
t 8835
 
4.0%
r 8212
 
3.7%
i 7308
 
3.3%
o 7256
 
3.2%
n 6972
 
3.1%
s 6018
 
2.7%
Other values (54) 116596
52.2%
Distinct5000
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size358.5 KiB
2024-12-20T15:45:59.577255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.238922
Min length10

Characters and Unicode

Total characters81357
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4999 ?
Unique (%)99.8%

Sample

1st row908.610.2711x8507
2nd row+1-408-938-0389x952
3rd row001-929-516-1919x39288
4th row8907712983
5th row9999921886
ValueCountFrequency (%)
449)651-5198x65597 11
 
0.2%
001-652-348-4765x0079 1
 
< 0.1%
864-925-8941 1
 
< 0.1%
306.753.6693x620 1
 
< 0.1%
841)652-5216x95122 1
 
< 0.1%
645.998.8051x74261 1
 
< 0.1%
908.610.2711x8507 1
 
< 0.1%
1-408-938-0389x952 1
 
< 0.1%
598.250.4181 1
 
< 0.1%
972-968-0237x0312 1
 
< 0.1%
Other values (4990) 4990
99.6%
2024-12-20T15:45:59.883523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7808
9.6%
1 6805
8.4%
0 6803
8.4%
3 6575
8.1%
5 6566
8.1%
8 6559
8.1%
7 6536
8.0%
9 6514
8.0%
2 6446
7.9%
4 6426
7.9%
Other values (6) 14319
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 7808
9.6%
1 6805
8.4%
0 6803
8.4%
3 6575
8.1%
5 6566
8.1%
8 6559
8.1%
7 6536
8.0%
9 6514
8.0%
2 6446
7.9%
4 6426
7.9%
Other values (6) 14319
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 7808
9.6%
1 6805
8.4%
0 6803
8.4%
3 6575
8.1%
5 6566
8.1%
8 6559
8.1%
7 6536
8.0%
9 6514
8.0%
2 6446
7.9%
4 6426
7.9%
Other values (6) 14319
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 7808
9.6%
1 6805
8.4%
0 6803
8.4%
3 6575
8.1%
5 6566
8.1%
8 6559
8.1%
7 6536
8.0%
9 6514
8.0%
2 6446
7.9%
4 6426
7.9%
Other values (6) 14319
17.6%

CustomerSegment
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size305.1 KiB
Gold
1692 
Silver
1683 
Bronze
1635 

Length

Max length6
Median length6
Mean length5.3245509
Min length4

Characters and Unicode

Total characters26676
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilver
2nd rowGold
3rd rowGold
4th rowGold
5th rowGold

Common Values

ValueCountFrequency (%)
Gold 1692
33.8%
Silver 1683
33.6%
Bronze 1635
32.6%

Length

2024-12-20T15:46:00.009297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T15:46:00.097912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
gold 1692
33.8%
silver 1683
33.6%
bronze 1635
32.6%

Most occurring characters

ValueCountFrequency (%)
l 3375
12.7%
o 3327
12.5%
r 3318
12.4%
e 3318
12.4%
d 1692
6.3%
G 1692
6.3%
i 1683
6.3%
S 1683
6.3%
v 1683
6.3%
B 1635
6.1%
Other values (2) 3270
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 3375
12.7%
o 3327
12.5%
r 3318
12.4%
e 3318
12.4%
d 1692
6.3%
G 1692
6.3%
i 1683
6.3%
S 1683
6.3%
v 1683
6.3%
B 1635
6.1%
Other values (2) 3270
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 3375
12.7%
o 3327
12.5%
r 3318
12.4%
e 3318
12.4%
d 1692
6.3%
G 1692
6.3%
i 1683
6.3%
S 1683
6.3%
v 1683
6.3%
B 1635
6.1%
Other values (2) 3270
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 3375
12.7%
o 3327
12.5%
r 3318
12.4%
e 3318
12.4%
d 1692
6.3%
G 1692
6.3%
i 1683
6.3%
S 1683
6.3%
v 1683
6.3%
B 1635
6.1%
Other values (2) 3270
12.3%

SupplierName
Categorical

High correlation 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size364.8 KiB
Gilbert, Miller and Lee
 
277
Mathis, Olson and Ryan
 
272
Smith PLC
 
272
Ramirez, Hicks and Lara
 
267
Higgins and Sons
 
263
Other values (15)
3659 

Length

Max length28
Median length26
Mean length17.535928
Min length9

Characters and Unicode

Total characters87855
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRodriguez, Winters and Perez
2nd rowLawson-Wilkins
3rd rowLee-Miller
4th rowSoto-Rivera
5th rowRodriguez, Winters and Perez

Common Values

ValueCountFrequency (%)
Gilbert, Miller and Lee 277
 
5.5%
Mathis, Olson and Ryan 272
 
5.4%
Smith PLC 272
 
5.4%
Ramirez, Hicks and Lara 267
 
5.3%
Higgins and Sons 263
 
5.2%
Rodriguez, Winters and Perez 263
 
5.2%
Campos-Murillo 262
 
5.2%
Lee-Miller 256
 
5.1%
White and Sons 255
 
5.1%
Roman-Chambers 254
 
5.1%
Other values (10) 2369
47.3%

Length

2024-12-20T15:46:00.198924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 2560
 
20.2%
sons 518
 
4.1%
jackson 479
 
3.8%
lee 277
 
2.2%
miller 277
 
2.2%
gilbert 277
 
2.2%
mathis 272
 
2.1%
ryan 272
 
2.1%
olson 272
 
2.1%
plc 272
 
2.1%
Other values (29) 7189
56.8%

Most occurring characters

ValueCountFrequency (%)
a 7812
 
8.9%
7655
 
8.7%
n 7283
 
8.3%
i 5371
 
6.1%
e 5340
 
6.1%
l 5285
 
6.0%
r 5244
 
6.0%
o 4882
 
5.6%
d 3549
 
4.0%
s 3338
 
3.8%
Other values (29) 32096
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7812
 
8.9%
7655
 
8.7%
n 7283
 
8.3%
i 5371
 
6.1%
e 5340
 
6.1%
l 5285
 
6.0%
r 5244
 
6.0%
o 4882
 
5.6%
d 3549
 
4.0%
s 3338
 
3.8%
Other values (29) 32096
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7812
 
8.9%
7655
 
8.7%
n 7283
 
8.3%
i 5371
 
6.1%
e 5340
 
6.1%
l 5285
 
6.0%
r 5244
 
6.0%
o 4882
 
5.6%
d 3549
 
4.0%
s 3338
 
3.8%
Other values (29) 32096
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7812
 
8.9%
7655
 
8.7%
n 7283
 
8.3%
i 5371
 
6.1%
e 5340
 
6.1%
l 5285
 
6.0%
r 5244
 
6.0%
o 4882
 
5.6%
d 3549
 
4.0%
s 3338
 
3.8%
Other values (29) 32096
36.5%

SupplierLocation
Categorical

High correlation 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size338.9 KiB
Lake Juanland
 
277
Herreraland
 
272
Lake Candiceton
 
272
Dustinmouth
 
267
West Johnbury
 
263
Other values (15)
3659 

Length

Max length16
Median length14
Mean length12.250898
Min length8

Characters and Unicode

Total characters61377
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIrwinhaven
2nd rowCalderonchester
3rd rowSouth Emilyview
4th rowWest Denise
5th rowIrwinhaven

Common Values

ValueCountFrequency (%)
Lake Juanland 277
 
5.5%
Herreraland 272
 
5.4%
Lake Candiceton 272
 
5.4%
Dustinmouth 267
 
5.3%
West Johnbury 263
 
5.2%
Irwinhaven 263
 
5.2%
Lawsontown 262
 
5.2%
South Emilyview 256
 
5.1%
West Scott 255
 
5.1%
Dennischester 254
 
5.1%
Other values (10) 2369
47.3%

Length

2024-12-20T15:46:00.304570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
west 963
 
13.3%
lake 549
 
7.6%
south 492
 
6.8%
juanland 277
 
3.8%
candiceton 272
 
3.7%
herreraland 272
 
3.7%
dustinmouth 267
 
3.7%
johnbury 263
 
3.6%
irwinhaven 263
 
3.6%
lawsontown 262
 
3.6%
Other values (14) 3379
46.5%

Most occurring characters

ValueCountFrequency (%)
e 7195
 
11.7%
n 5576
 
9.1%
t 5198
 
8.5%
a 4365
 
7.1%
r 4007
 
6.5%
o 3998
 
6.5%
i 3436
 
5.6%
s 3186
 
5.2%
h 2512
 
4.1%
2249
 
3.7%
Other values (24) 19655
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7195
 
11.7%
n 5576
 
9.1%
t 5198
 
8.5%
a 4365
 
7.1%
r 4007
 
6.5%
o 3998
 
6.5%
i 3436
 
5.6%
s 3186
 
5.2%
h 2512
 
4.1%
2249
 
3.7%
Other values (24) 19655
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7195
 
11.7%
n 5576
 
9.1%
t 5198
 
8.5%
a 4365
 
7.1%
r 4007
 
6.5%
o 3998
 
6.5%
i 3436
 
5.6%
s 3186
 
5.2%
h 2512
 
4.1%
2249
 
3.7%
Other values (24) 19655
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7195
 
11.7%
n 5576
 
9.1%
t 5198
 
8.5%
a 4365
 
7.1%
r 4007
 
6.5%
o 3998
 
6.5%
i 3436
 
5.6%
s 3186
 
5.2%
h 2512
 
4.1%
2249
 
3.7%
Other values (24) 19655
32.0%

SupplierContact
Categorical

High correlation  Missing 

Distinct20
Distinct (%)0.4%
Missing484
Missing (%)9.7%
Memory size349.9 KiB
(906)306-1777x264
 
248
+1-619-677-8121x28226
 
247
230-568-4141
 
240
459-301-8580
 
240
822-222-4939
 
239
Other values (15)
3312 

Length

Max length21
Median length18
Mean length15.280822
Min length10

Characters and Unicode

Total characters69161
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6538306661
2nd row+1-588-750-7646
3rd row805-650-6257x5876
4th row6538306661
5th row805-650-6257x5876

Common Values

ValueCountFrequency (%)
(906)306-1777x264 248
 
5.0%
+1-619-677-8121x28226 247
 
4.9%
230-568-4141 240
 
4.8%
459-301-8580 240
 
4.8%
822-222-4939 239
 
4.8%
853-738-3918 238
 
4.8%
6538306661 236
 
4.7%
001-324-909-1637x2831 234
 
4.7%
+1-588-750-7646 232
 
4.6%
288-572-7043 231
 
4.6%
Other values (10) 2141
42.7%
(Missing) 484
 
9.7%

Length

2024-12-20T15:46:00.403254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
906)306-1777x264 248
 
5.5%
1-619-677-8121x28226 247
 
5.5%
230-568-4141 240
 
5.3%
459-301-8580 240
 
5.3%
822-222-4939 239
 
5.3%
853-738-3918 238
 
5.3%
6538306661 236
 
5.2%
001-324-909-1637x2831 234
 
5.2%
1-588-750-7646 232
 
5.1%
288-572-7043 231
 
5.1%
Other values (10) 2141
47.3%

Most occurring characters

ValueCountFrequency (%)
- 8354
12.1%
7 7779
11.2%
2 7039
10.2%
6 6548
9.5%
5 5741
8.3%
8 5650
8.2%
0 5392
7.8%
1 5292
7.7%
3 5248
7.6%
9 4387
6.3%
Other values (6) 7731
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 8354
12.1%
7 7779
11.2%
2 7039
10.2%
6 6548
9.5%
5 5741
8.3%
8 5650
8.2%
0 5392
7.8%
1 5292
7.7%
3 5248
7.6%
9 4387
6.3%
Other values (6) 7731
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 8354
12.1%
7 7779
11.2%
2 7039
10.2%
6 6548
9.5%
5 5741
8.3%
8 5650
8.2%
0 5392
7.8%
1 5292
7.7%
3 5248
7.6%
9 4387
6.3%
Other values (6) 7731
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 8354
12.1%
7 7779
11.2%
2 7039
10.2%
6 6548
9.5%
5 5741
8.3%
8 5650
8.2%
0 5392
7.8%
1 5292
7.7%
3 5248
7.6%
9 4387
6.3%
Other values (6) 7731
11.2%

ShipperName
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size299.5 KiB
Group
861 
and Sons
851 
LLC
846 
PLC
832 
Ltd
814 

Length

Max length8
Median length3
Mean length4.193014
Min length3

Characters and Unicode

Total characters21007
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowand Sons
2nd rowPLC
3rd rowPLC
4th rowLLC
5th rowLtd

Common Values

ValueCountFrequency (%)
Group 861
17.2%
and Sons 851
17.0%
LLC 846
16.9%
PLC 832
16.6%
Ltd 814
16.2%
Inc 806
16.1%

Length

2024-12-20T15:46:00.495664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T15:46:00.581934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
group 861
14.7%
and 851
14.5%
sons 851
14.5%
llc 846
14.4%
plc 832
14.2%
ltd 814
13.9%
inc 806
13.8%

Most occurring characters

ValueCountFrequency (%)
L 3338
15.9%
n 2508
11.9%
o 1712
 
8.1%
C 1678
 
8.0%
d 1665
 
7.9%
G 861
 
4.1%
u 861
 
4.1%
p 861
 
4.1%
r 861
 
4.1%
851
 
4.1%
Other values (7) 5811
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 3338
15.9%
n 2508
11.9%
o 1712
 
8.1%
C 1678
 
8.0%
d 1665
 
7.9%
G 861
 
4.1%
u 861
 
4.1%
p 861
 
4.1%
r 861
 
4.1%
851
 
4.1%
Other values (7) 5811
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 3338
15.9%
n 2508
11.9%
o 1712
 
8.1%
C 1678
 
8.0%
d 1665
 
7.9%
G 861
 
4.1%
u 861
 
4.1%
p 861
 
4.1%
r 861
 
4.1%
851
 
4.1%
Other values (7) 5811
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 3338
15.9%
n 2508
11.9%
o 1712
 
8.1%
C 1678
 
8.0%
d 1665
 
7.9%
G 861
 
4.1%
u 861
 
4.1%
p 861
 
4.1%
r 861
 
4.1%
851
 
4.1%
Other values (7) 5811
27.7%

ShippingMethod
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size298.6 KiB
Air
1707 
Ground
1693 
Sea
1610 

Length

Max length6
Median length3
Mean length4.0137725
Min length3

Characters and Unicode

Total characters20109
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGround
2nd rowAir
3rd rowSea
4th rowAir
5th rowSea

Common Values

ValueCountFrequency (%)
Air 1707
34.1%
Ground 1693
33.8%
Sea 1610
32.1%

Length

2024-12-20T15:46:00.690248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T15:46:00.769169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
air 1707
34.1%
ground 1693
33.8%
sea 1610
32.1%

Most occurring characters

ValueCountFrequency (%)
r 3400
16.9%
A 1707
8.5%
i 1707
8.5%
G 1693
8.4%
o 1693
8.4%
u 1693
8.4%
n 1693
8.4%
d 1693
8.4%
S 1610
8.0%
e 1610
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 3400
16.9%
A 1707
8.5%
i 1707
8.5%
G 1693
8.4%
o 1693
8.4%
u 1693
8.4%
n 1693
8.4%
d 1693
8.4%
S 1610
8.0%
e 1610
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 3400
16.9%
A 1707
8.5%
i 1707
8.5%
G 1693
8.4%
o 1693
8.4%
u 1693
8.4%
n 1693
8.4%
d 1693
8.4%
S 1610
8.0%
e 1610
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 3400
16.9%
A 1707
8.5%
i 1707
8.5%
G 1693
8.4%
o 1693
8.4%
u 1693
8.4%
n 1693
8.4%
d 1693
8.4%
S 1610
8.0%
e 1610
8.0%

QuantitySold
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.827146
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:00.860421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median50
Q376
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.523303
Coefficient of variation (CV)0.56118247
Kurtosis-1.1823428
Mean50.827146
Median Absolute Deviation (MAD)24
Skewness0.00030494085
Sum254644
Variance813.57882
MonotonicityNot monotonic
2024-12-20T15:46:00.967862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 70
 
1.4%
39 66
 
1.3%
80 65
 
1.3%
87 64
 
1.3%
73 63
 
1.3%
26 62
 
1.2%
92 60
 
1.2%
40 60
 
1.2%
30 59
 
1.2%
16 59
 
1.2%
Other values (90) 4382
87.5%
ValueCountFrequency (%)
1 46
0.9%
2 44
0.9%
3 51
1.0%
4 41
0.8%
5 41
0.8%
6 47
0.9%
7 41
0.8%
8 39
0.8%
9 52
1.0%
10 48
1.0%
ValueCountFrequency (%)
100 47
0.9%
99 57
1.1%
98 42
0.8%
97 46
0.9%
96 50
1.0%
95 48
1.0%
94 52
1.0%
93 52
1.0%
92 60
1.2%
91 47
0.9%

TotalAmount
Real number (ℝ)

High correlation 

Distinct1848
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28851.53
Minimum46.23
Maximum99513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:01.083168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum46.23
5-th percentile1546.28
Q18864.13
median23546.135
Q342985.95
95-th percentile75624.129
Maximum99513
Range99466.77
Interquartile range (IQR)34121.82

Descriptive statistics

Standard deviation23139.578
Coefficient of variation (CV)0.80202258
Kurtosis-0.20417134
Mean28851.53
Median Absolute Deviation (MAD)16452.715
Skewness0.78550937
Sum1.4454616 × 108
Variance5.3544008 × 108
MonotonicityNot monotonic
2024-12-20T15:46:01.190409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33631.59 12
 
0.2%
48761.37 9
 
0.2%
76122.4 9
 
0.2%
64590.9 9
 
0.2%
31522.86 9
 
0.2%
77963.56 8
 
0.2%
44359.8 8
 
0.2%
17472.48 8
 
0.2%
3491.93 8
 
0.2%
39653.74 7
 
0.1%
Other values (1838) 4923
98.3%
ValueCountFrequency (%)
46.23 1
 
< 0.1%
68.01 1
 
< 0.1%
92.46 1
 
< 0.1%
138.69 4
0.1%
184.92 3
0.1%
204.03 2
< 0.1%
228.82 2
< 0.1%
231.15 1
 
< 0.1%
268.61 4
0.1%
272.04 2
< 0.1%
ValueCountFrequency (%)
99513 2
 
< 0.1%
98517.87 2
 
< 0.1%
97522.74 2
 
< 0.1%
96527.61 2
 
< 0.1%
95532.48 2
 
< 0.1%
95153 6
0.1%
94537.35 1
 
< 0.1%
94201.47 3
0.1%
93542.22 2
 
< 0.1%
93249.94 3
0.1%

DiscountAmount
Real number (ℝ)

Distinct4353
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.425236
Minimum0.01
Maximum298.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:01.288336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.538
Q125.685
median69.345
Q3129.2775
95-th percentile228.5685
Maximum298.11
Range298.1
Interquartile range (IQR)103.5925

Descriptive statistics

Standard deviation70.574899
Coefficient of variation (CV)0.8166006
Kurtosis-0.26615701
Mean86.425236
Median Absolute Deviation (MAD)49.605
Skewness0.79048336
Sum432990.43
Variance4980.8163
MonotonicityNot monotonic
2024-12-20T15:46:01.511104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.77 11
 
0.2%
7.18 4
 
0.1%
17.79 4
 
0.1%
2.83 4
 
0.1%
96.71 4
 
0.1%
11.5 4
 
0.1%
88.78 4
 
0.1%
7.62 4
 
0.1%
8.21 3
 
0.1%
12.37 3
 
0.1%
Other values (4343) 4965
99.1%
ValueCountFrequency (%)
0.01 1
< 0.1%
0.05 1
< 0.1%
0.06 1
< 0.1%
0.07 1
< 0.1%
0.09 1
< 0.1%
0.13 2
< 0.1%
0.14 2
< 0.1%
0.15 1
< 0.1%
0.17 1
< 0.1%
0.18 1
< 0.1%
ValueCountFrequency (%)
298.11 1
< 0.1%
296.11 1
< 0.1%
295.18 1
< 0.1%
295.08 1
< 0.1%
293.84 1
< 0.1%
293.28 1
< 0.1%
291.22 1
< 0.1%
290.07 1
< 0.1%
289.26 1
< 0.1%
289.11 1
< 0.1%

NetAmount
Real number (ℝ)

High correlation 

Distinct4999
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28765.104
Minimum33.6
Maximum99276.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:01.622266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum33.6
5-th percentile1497.0885
Q18774.435
median23424.75
Q342886.6
95-th percentile75463.623
Maximum99276.95
Range99243.35
Interquartile range (IQR)34112.165

Descriptive statistics

Standard deviation23112.636
Coefficient of variation (CV)0.80349563
Kurtosis-0.20322873
Mean28765.104
Median Absolute Deviation (MAD)16388.65
Skewness0.78626283
Sum1.4411317 × 108
Variance5.3419393 × 108
MonotonicityNot monotonic
2024-12-20T15:46:01.731551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33542.82 11
 
0.2%
33682.91 2
 
< 0.1%
48789.08 1
 
< 0.1%
53284.87 1
 
< 0.1%
25637.75 1
 
< 0.1%
2172.89 1
 
< 0.1%
22715.3 1
 
< 0.1%
39019.81 1
 
< 0.1%
6238.81 1
 
< 0.1%
19967.29 1
 
< 0.1%
Other values (4989) 4989
99.6%
ValueCountFrequency (%)
33.6 1
< 0.1%
57.81 1
< 0.1%
83.22 1
< 0.1%
125.11 1
< 0.1%
126.83 1
< 0.1%
128.04 1
< 0.1%
129.8 1
< 0.1%
175.81 1
< 0.1%
178.04 1
< 0.1%
183.97 1
< 0.1%
ValueCountFrequency (%)
99276.95 1
< 0.1%
99246.13 1
< 0.1%
98471.73 1
< 0.1%
98299.72 1
< 0.1%
97494.61 1
< 0.1%
97409.48 1
< 0.1%
96325.22 1
< 0.1%
96267.15 1
< 0.1%
95421.03 1
< 0.1%
95260.03 1
< 0.1%

StockReceived
Real number (ℝ)

High correlation 

Distinct451
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.07405
Minimum50
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:01.829974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile72
Q1164
median279
Q3393
95-th percentile478
Maximum500
Range450
Interquartile range (IQR)229

Descriptive statistics

Standard deviation130.3307
Coefficient of variation (CV)0.47038218
Kurtosis-1.2123356
Mean277.07405
Median Absolute Deviation (MAD)115
Skewness-0.029436097
Sum1388141
Variance16986.091
MonotonicityNot monotonic
2024-12-20T15:46:01.933116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 23
 
0.5%
290 23
 
0.5%
401 22
 
0.4%
258 21
 
0.4%
143 21
 
0.4%
341 21
 
0.4%
185 21
 
0.4%
464 21
 
0.4%
430 20
 
0.4%
281 20
 
0.4%
Other values (441) 4797
95.7%
ValueCountFrequency (%)
50 11
0.2%
51 14
0.3%
52 6
0.1%
53 14
0.3%
54 6
0.1%
55 11
0.2%
56 4
 
0.1%
57 13
0.3%
58 10
0.2%
59 8
0.2%
ValueCountFrequency (%)
500 20
0.4%
499 12
0.2%
498 5
 
0.1%
497 7
 
0.1%
496 19
0.4%
495 6
 
0.1%
494 9
0.2%
493 9
0.2%
492 14
0.3%
491 13
0.3%

StockSold
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.11697
Minimum1
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:02.041012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q149
median109
Q3209
95-th percentile362
Maximum498
Range497
Interquartile range (IQR)160

Descriptive statistics

Standard deviation111.01637
Coefficient of variation (CV)0.79800741
Kurtosis-0.067428197
Mean139.11697
Median Absolute Deviation (MAD)71
Skewness0.87581816
Sum696976
Variance12324.634
MonotonicityNot monotonic
2024-12-20T15:46:02.150332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 34
 
0.7%
7 34
 
0.7%
56 34
 
0.7%
12 33
 
0.7%
64 33
 
0.7%
38 33
 
0.7%
60 32
 
0.6%
27 32
 
0.6%
49 32
 
0.6%
50 31
 
0.6%
Other values (451) 4682
93.5%
ValueCountFrequency (%)
1 24
0.5%
2 22
0.4%
3 24
0.5%
4 22
0.4%
5 27
0.5%
6 25
0.5%
7 34
0.7%
8 24
0.5%
9 26
0.5%
10 24
0.5%
ValueCountFrequency (%)
498 1
< 0.1%
494 2
< 0.1%
493 1
< 0.1%
486 1
< 0.1%
485 2
< 0.1%
483 1
< 0.1%
482 1
< 0.1%
477 1
< 0.1%
473 2
< 0.1%
469 1
< 0.1%

StockOnHand
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.95709
Minimum0
Maximum490
Zeros31
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size39.3 KiB
2024-12-20T15:46:02.254064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q149
median107
Q3206
95-th percentile359.55
Maximum490
Range490
Interquartile range (IQR)157

Descriptive statistics

Standard deviation110.36069
Coefficient of variation (CV)0.79996393
Kurtosis-0.018666969
Mean137.95709
Median Absolute Deviation (MAD)70
Skewness0.88968298
Sum691165
Variance12179.482
MonotonicityNot monotonic
2024-12-20T15:46:02.362325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 38
 
0.8%
50 37
 
0.7%
42 36
 
0.7%
10 35
 
0.7%
15 34
 
0.7%
34 34
 
0.7%
31 32
 
0.6%
0 31
 
0.6%
19 31
 
0.6%
38 31
 
0.6%
Other values (454) 4671
93.2%
ValueCountFrequency (%)
0 31
0.6%
1 26
0.5%
2 29
0.6%
3 20
0.4%
4 26
0.5%
5 22
0.4%
6 25
0.5%
7 22
0.4%
8 24
0.5%
9 28
0.6%
ValueCountFrequency (%)
490 1
< 0.1%
488 1
< 0.1%
484 1
< 0.1%
482 2
< 0.1%
481 2
< 0.1%
476 1
< 0.1%
474 1
< 0.1%
473 2
< 0.1%
470 1
< 0.1%
469 1
< 0.1%

Interactions

2024-12-20T15:45:55.777272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.416405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.968981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.523455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.175668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.715890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.249314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.852310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.505734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.049340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.602189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.248230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.795930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.327807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.926944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.583435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.122196image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.685297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.327748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.872440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.406868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:56.008666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.662577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.205093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.764917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.408667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.950486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.483096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:56.082345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.741507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.284496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.846680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.487340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.026330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.559002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:56.156031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.817101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.367152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.925944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.561897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.098462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.629662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:56.233140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:52.892402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:53.445012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.001651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:54.641822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.174053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T15:45:55.705738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-20T15:46:02.442087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
CustomerSegmentDiscountAmountNetAmountProductCategoryProductNameProductPriceProductSubCategoryQuantitySoldShipperNameShippingMethodStockOnHandStockReceivedStockSoldSupplierContactSupplierLocationSupplierNameTotalAmount
CustomerSegment1.0000.0160.0320.0140.0240.0530.0360.0000.0190.0000.0000.0000.0210.0360.0310.0310.033
DiscountAmount0.0161.0000.4130.2280.2800.2640.251-0.0080.0000.019-0.0040.0070.0130.0170.0070.0070.417
NetAmount0.0320.4131.0000.2220.2790.2630.2500.6960.0000.039-0.0080.0070.0150.0000.0000.0001.000
ProductCategory0.0140.2280.2221.0000.8050.7520.8450.0230.0000.0000.0000.0000.0000.0190.0000.0000.221
ProductName0.0240.2800.2790.8051.0000.8280.9520.0210.0000.0340.0100.0000.0160.0110.0010.0010.279
ProductPrice0.0530.2640.2630.7520.8281.0000.8930.0000.0000.0050.0000.0000.0000.0070.0000.0000.262
ProductSubCategory0.0360.2510.2500.8450.9520.8931.0000.0130.0000.0100.0000.0000.0190.0100.0000.0000.250
QuantitySold0.000-0.0080.6960.0230.0210.0000.0131.0000.0000.028-0.013-0.0010.0020.0000.0000.0000.694
ShipperName0.0190.0000.0000.0000.0000.0000.0000.0001.0000.0000.0100.0000.0000.0410.0390.0390.000
ShippingMethod0.0000.0190.0390.0000.0340.0050.0100.0280.0001.0000.0000.0240.0000.0000.0000.0000.037
StockOnHand0.000-0.004-0.0080.0000.0100.0000.000-0.0130.0100.0001.0000.561-0.2550.0130.0020.002-0.008
StockReceived0.0000.0070.0070.0000.0000.0000.000-0.0010.0000.0240.5611.0000.5720.0000.0000.0000.007
StockSold0.0210.0130.0150.0000.0160.0000.0190.0020.0000.000-0.2550.5721.0000.0000.0000.0000.015
SupplierContact0.0360.0170.0000.0190.0110.0070.0100.0000.0410.0000.0130.0000.0001.0001.0001.0000.000
SupplierLocation0.0310.0070.0000.0000.0010.0000.0000.0000.0390.0000.0020.0000.0001.0001.0001.0000.000
SupplierName0.0310.0070.0000.0000.0010.0000.0000.0000.0390.0000.0020.0000.0001.0001.0001.0000.000
TotalAmount0.0330.4171.0000.2210.2790.2620.2500.6940.0000.037-0.0080.0070.0150.0000.0000.0001.000

Missing values

2024-12-20T15:45:56.358740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-20T15:45:56.622682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-20T15:45:56.814806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateProductNameProductCategoryProductSubCategoryProductPriceCustomerNameCustomerEmailCustomerAddressCustomerPhoneCustomerSegmentSupplierNameSupplierLocationSupplierContactShipperNameShippingMethodQuantitySoldTotalAmountDiscountAmountNetAmountStockReceivedStockSoldStockOnHand
02023-09-14NathanielElectronicsCamera0.01Colleen Kellymaryhurst@example.org354 Mcdowell Turnpike, Port Charles, CT 95318908.610.2711x8507SilverRodriguez, Winters and PerezIrwinhaven6538306661and SonsGround4931965.15121.0731844.08475127348
12023-02-11NonExistentProductElectronicsMobile847.43Joel Wrightsandersvictoria@example.org24740 Fox Villages, New Tracie, MA 53038+1-408-938-0389x952GoldLawson-WilkinsCalderonchesterNaNPLCAir7361862.3991.0961771.30487243244
22021-11-12AngelaInvalidCategoryAction Figures386.57Thomas Sawyerospence@example.net769 Joe Trail, East Terri, CA 43813001-929-516-1919x39288GoldLee-MillerSouth Emilyview+1-588-750-7646PLCSea8934404.7310.5634394.17341188153
311-15-2021AmyHome & GardenDecor364.01Tyler Gardnerchristopherjohnson@example.com27783 Olivia Centers, Williamsmouth, AL 098098907712983GoldSoto-RiveraWest Denise805-650-6257x5876LLCAir31092.0369.061022.97500124376
42023-04-22NathanielElectronicsCamera652.35Meagan Petersonepowell@example.net25357 Blackwell Locks, Andreabury, MH 278579999921886GoldRodriguez, Winters and PerezIrwinhaven6538306661LtdSea7548926.25137.1748789.0842935178
52022-12-08NonExistentProductElectronicsMobile847.43Justin HodgeNaNPSC 1552, Box 7906, APO AP 50694001-691-442-5371x97648GoldSoto-RiveraWest Denise805-650-6257x5876IncGround6353388.09103.2253284.87796118
62023-09-13JustinElectronicsHeadphones66005.0Kelly Williamsphilip97@example.com5881 Shawn Fords Apt. 256, Martinezbury, NH 01010001-275-372-4620SilverSoto-RiveraWest Denise805-650-6257x5876and SonsGround3925741.95104.2025637.7533929643
72023-07-19NonExistentProductElectronicsCamera652.35Natasha Thomasrcole@example.org040 Lopez Crossing Apt. 758, Victormouth, RI 51198998-812-6450x26711SilverCampos-MurilloLawsontown822-222-4939GroupGround6039141.00121.1939019.8130553252
82023-08-20ShannonHome & GardenGardening Tools68.01Jessica SanchezNaN455 Mary Path, Cobbberg, PW 26662972-968-0237x0312GoldWagner, Jackson and HoltPort Sarahview+1-362-752-2777x1817IncAir926256.9218.116238.81440338102
92023-07-15NonExistentProductHome & GardenDecor364.01Nathan GoodNaN27534 Jennifer Burgs Suite 449, West Lauraville, AK 18548322.403.9678x196GoldWhite and SonsWest Scott288-572-7043PLCAir5520020.5553.2619967.292602546
DateProductNameProductCategoryProductSubCategoryProductPriceCustomerNameCustomerEmailCustomerAddressCustomerPhoneCustomerSegmentSupplierNameSupplierLocationSupplierContactShipperNameShippingMethodQuantitySoldTotalAmountDiscountAmountNetAmountStockReceivedStockSoldStockOnHand
50002023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50012023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50022023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50032023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50042023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50052023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50062023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50072023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50082023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942
50092023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.8240135942

Duplicate rows

Most frequently occurring

DateProductNameProductCategoryProductSubCategoryProductPriceCustomerNameCustomerEmailCustomerAddressCustomerPhoneCustomerSegmentSupplierNameSupplierLocationSupplierContactShipperNameShippingMethodQuantitySoldTotalAmountDiscountAmountNetAmountStockReceivedStockSoldStockOnHand# duplicates
02023-08-24AngelaToysAction Figures386.57Bradley Smithgarciashawn@example.comUnit 6079 Box 2184, DPO AP 21061(449)651-5198x65597GoldRoman-ChambersDennischester001-324-909-1637x2831LLCGround8733631.5988.7733542.824013594211